3 citations · 6 across the 9 of their papers we have counts for
6 papers · 1 filter
Constraint-Informed Active Learning for End-to-End ACOPF Optimization Proxies
Miao Li, Michael Klamkin, Pascal Van Hentenryck +2
This paper studies optimization proxies, machine learning (ML) models trained to efficiently predict optimal solutions for AC Optimal Power Flow (ACOPF) problems. While promising,…
Self-Certifying Primal-Dual Optimization Proxies for Large-Scale Batch Economic Dispatch
Michael Klamkin, Mathieu Tanneau, Pascal Van Hentenryck
Recent research has shown that optimization proxies can be trained to high fidelity, achieving average optimality gaps under 1% for large-scale problems. However, worst-case analys…
PGLearn -- An Open-Source Learning Toolkit for Optimal Power Flow
Michael Klamkin, Mathieu Tanneau, Pascal Van Hentenryck
Machine Learning (ML) techniques for Optimal Power Flow (OPF) problems have recently garnered significant attention, reflecting a broader trend of leveraging ML to approximate and/…
DualSchool: How Reliable are LLMs for Optimization Education?
Michael Klamkin, Arnaud Deza, Sikai Cheng +2
Consider the following task taught in introductory optimization courses which addresses challenges articulated by the community at the intersection of (generative) AI and OR: gener…
Differentiable Optimization for Deep Learning-Enhanced DC Approximation of AC Optimal Power Flow
Andrew Rosemberg, Michael Klamkin, Pascal Van Hentenryck
The growing scale of power systems and the increasing uncertainty introduced by renewable energy sources necessitates novel optimization techniques that are significantly faster an…
Tightening optimality gap with confidence through conformal prediction
Miao Li, Michael Klamkin, Russell Bent +1
Decision makers routinely use constrained optimization technology to plan and operate complex systems like global supply chains or power grids. In this context, practitioners must…